White
WhiteMat = whiteningMatrix(cov(noclassData), method="PCA")
sum(is.na(WhiteMat))
[1] 1928
tokeep <- apply(is.na(WhiteMat),1,sum) == 0
WhiteMat <- WhiteMat[tokeep,]
sum(is.na(WhiteMat))
[1] 0
sum(apply(abs(WhiteMat),1,sum) > 1.0e6)
[1] 9
tokeep <- apply(abs(WhiteMat),1,sum) < 1.0e6
WhiteMat <- WhiteMat[tokeep,]
sum(apply(abs(WhiteMat),1,sum) > 1.0e6)
[1] 0
pander::pander(c(ncol=ncol(WhiteMat),nrow=nrow(WhiteMat)))
pander::pander(WhiteMat[1:TopVariables,1:TopVariables])
| L1 |
1.49e-24 |
1.78e-23 |
1.43e-24 |
1.25e-24 |
5.39e-23 |
| L2 |
1.54e-22 |
1.77e-21 |
9.11e-23 |
2.96e-23 |
2.76e-21 |
| L3 |
5.13e-17 |
1.37e-16 |
5.56e-17 |
6.35e-17 |
2.39e-16 |
| L4 |
1.66e-16 |
3.50e-16 |
1.65e-16 |
1.61e-16 |
8.83e-16 |
| L5 |
3.25e-16 |
1.03e-15 |
2.97e-16 |
5.75e-16 |
8.24e-16 |
PCAWhite_Train <- as.data.frame(cbind(tcrossprod(as.matrix(noclassData), WhiteMat),trainDataFrame[,notcorr]))
colnames(PCAWhite_Train) <- c(colnames(tcrossprod(as.matrix(noclassData), WhiteMat)),notcorr)
sum(is.na(PCAWhite_Train))
[1] 0
PCAWhitePredicted <- as.data.frame(cbind(tcrossprod(as.matrix(noclassTestData), WhiteMat),testDataFrame[,notcorr]))
colnames(PCAWhitePredicted) <- c(colnames(tcrossprod(as.matrix(noclassTestData), WhiteMat)),notcorr)
sum(is.na(PCAWhitePredicted))
[1] 0
iscontinous <- colnames(PCAWhitePredicted)[sapply(apply(PCAWhitePredicted,2,unique),length) > 5] ## Only variables with enough
varlistWhite <- iscontinous
univarWhite <- uniRankVar(varlistWhite,
paste(outcome,"~1"),
outcome,
PCAWhite_Train,
rankingTest = "CStat")
100 : L100 200 : L200 300 : L300 400 : L400 500 : IT_BRV_skewness
univarWhiteTest <- uniRankVar(varlistWhite,
paste(outcome,"~1"),
outcome,
PCAWhite_Train,
testData=PCAWhitePredicted,
rankingTest = "CStat")
100 : L100 200 : L200 300 : L300 400 : L400 500 : IT_BRV_skewness
univarWhite$orderframe$BACC <- (univarWhite$orderframe$Sensitivity + univarWhite$orderframe$Specificity)/2.0
univarWhiteTest$orderframe$BACC <- (univarWhiteTest$orderframe$Sensitivity + univarWhiteTest$orderframe$Specificity)/2.0
Correlation
Matrices
RAW
par(cex=1.0,cex.main=0.8)
breaks <- c(0:5)/5.0;
cormat <- cor(testDataFrame,method="spearman")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
pander::pander(max(abs(cormat)))
1
pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))
pander::pander(c(Raw_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
breaks = breaks,
main = "Raw Correlation",
cexRow = 0.25,
cexCol = 0.25,
srtCol=35,
srtRow=75,
key.title=NA,
key.xlab="Spearman Correlation",
xlab="Feature", ylab="Feature"
)

#hist(cormat,freq=FALSE,
# density=NULL,
# xlim=c(-1,1),
# ylim=c(0,4.0),
# main="Raw Correlation",xlab="Spearman Correlation")
rawDen <- density(cormat,from=-1,to=1)
par(op)
IDeA Blind
par(cex=1.0,cex.main=0.8)
## Train Correlation
cormat <- cor(DEdataframe,method="pearson")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
#pander::pander(colnames(cormat)[apply(abs(cormat),2,max)>rhoThreshold])
pander::pander(c(Train=max(abs(cormat))))
pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))
| 0.103 |
0.166 |
0.242 |
0.351 |
0.596 |
pander::pander(c(IDeA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))
## Test Correlation
cormat <- cor(predTestDe,method="spearman")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
pander::pander(c(Test=max(abs(cormat))))
pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))
| 0.153 |
0.252 |
0.418 |
0.603 |
0.821 |
pander::pander(c(IDeA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
breaks = breaks,
main = "Correlation after IDeA",
cexRow = 0.25,
cexCol = 0.25,
srtCol=35,
srtRow=75,
key.title=NA,
key.xlab="Spearman Correlation",
xlab="Feature", ylab="Feature")

#hist(cormat,freq=FALSE,
# density=NULL,
# xlim=c(-1,1),
# ylim=c(0,4.0),
# main="Correlation after IDeA",xlab="Spearman Correlation")
DeDen <- density(cormat,from=-1,to=1)
par(op)
IDeA
Blind/Spearman
par(cex=1.0,cex.main=0.8)
## Train Correlation
cormat <- cor(DEdataframeSpear,method="pearson")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
#pander::pander(colnames(cormat)[apply(abs(cormat),2,max)>rhoThreshold])
pander::pander(c(Train=max(abs(cormat))))
pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))
| 0.104 |
0.171 |
0.266 |
0.413 |
0.744 |
pander::pander(c(IDeA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))
## Test Correlation
cormat <- cor(predTestDeSpear,method="spearman")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
pander::pander(c(Test=max(abs(cormat))))
pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))
| 0.152 |
0.237 |
0.396 |
0.597 |
0.821 |
pander::pander(c(IDeA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
breaks = breaks,
main = "Correlation after IDeA:Spearman",
cexRow = 0.25,
cexCol = 0.25,
srtCol=35,
srtRow=75,
key.title=NA,
key.xlab="Spearman Correlation",
xlab="Feature", ylab="Feature")

#hist(cormat,freq=FALSE,
# density=NULL,
# xlim=c(-1,1),
# ylim=c(0,4.0),
# main="Correlation after IDeA",xlab="Spearman Correlation")
DeSpearDen <- density(cormat,from=-1,to=1)
par(op)
IDeA Driven
par(cex=1.0,cex.main=0.8)
## Train Correlation
cormat <- cor(DriDEdataframe,method="pearson")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
#pander::pander(colnames(cormat)[apply(abs(cormat),2,max)>rhoThreshold])
pander::pander(c(Train=max(abs(cormat))))
pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))
| 0.103 |
0.167 |
0.246 |
0.366 |
0.688 |
pander::pander(c(IDeA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))
## Test Correlation
cormat <- cor(DriDEdataframe,method="spearman")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
pander::pander(c(Test=max(abs(cormat))))
pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))
| 0.152 |
0.245 |
0.408 |
0.591 |
0.821 |
pander::pander(c(IDeA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
breaks = breaks,
main = "Correlation after Driven-IDeA",
cexRow = 0.25,
cexCol = 0.25,
srtCol=35,
srtRow=75,
key.title=NA,
key.xlab="Spearman Correlation",
xlab="Feature", ylab="Feature")

#hist(cormat,freq=FALSE,
# density=NULL,
# xlim=c(-1,1),
# ylim=c(0,4.0),
# main="Correlation after Driven-IDeA",xlab="Spearman Correlation")
DeDrivDen <- density(cormat,from=-1,to=1)
par(op)
IDeA Spearman
par(cex=1.0,cex.main=0.8)
## Train Correlation
cormat <- cor(DriDEdataframeSpear,method="pearson")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
#pander::pander(colnames(cormat)[apply(abs(cormat),2,max)>rhoThreshold])
pander::pander(c(Train=max(abs(cormat))))
pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))
| 0.107 |
0.185 |
0.302 |
0.502 |
0.997 |
pander::pander(c(IDeAS_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))
## Test Correlation
cormat <- cor(predTestDriSpear,method="spearman")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
pander::pander(c(Test=max(abs(cormat))))
pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))
| 0.16 |
0.261 |
0.469 |
0.754 |
0.996 |
pander::pander(c(IDeAS_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
breaks = breaks,
main = "Correlation: Driven/Spearman",
cexRow = 0.25,
cexCol = 0.25,
srtCol=35,
srtRow=75,
key.title=NA,
key.xlab="Spearman Correlation",
xlab="Feature", ylab="Feature")

#hist(cormat,freq=FALSE,
# density=NULL,
# xlim=c(-1,1),
# ylim=c(0,4.0),
# main="Correlation after IDeA with Spearman",xlab="Spearman Correlation")
DeDrivSpearDen <- density(cormat,from=-1,to=1)
par(op)
PCA
par(cex=1.0,cex.main=0.8)
## Train Correlation
cormat <- cor(PCA_Train,method="pearson")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
pander::pander(c(Train=max(abs(cormat))))
pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))
| 0.173 |
0.285 |
0.401 |
0.53 |
0.792 |
pander::pander(c(PCA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))
## Test Correlation
cormat <- cor(PCA_Predicted,method="spearman")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
pander::pander(c(Test=max(abs(cormat))))
pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))
| 0.304 |
0.453 |
0.637 |
0.728 |
0.934 |
pander::pander(c(PCA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
breaks = breaks,
main = "Correlation after PCA",
cexRow = 0.25,
cexCol = 0.25,
srtCol=35,
srtRow=75,
key.title=NA,
key.xlab="Spearman Correlation",
xlab="Feature", ylab="Feature")

#hist(cormat,freq=FALSE,
# density=NULL,
# xlim=c(-1,1),
# ylim=c(0,4.0),
# main="Correlation after PCA",xlab="Spearman Correlation")
PCADen <- density(cormat,from=-1,to=1)
par(op)
EFA
par(cex=1.0,cex.main=0.8)
## Train Correlation
cormat <- cor(EFA_Train,method="pearson")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
pander::pander(c(Train=max(abs(cormat))))
pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))
| 0.172 |
0.287 |
0.403 |
0.53 |
0.789 |
pander::pander(c(EFA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))
## Test Correlation
cormat <- cor(EFA_Predicted,method="spearman")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
pander::pander(c(Test=max(abs(cormat))))
pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))
| 0.307 |
0.459 |
0.637 |
0.735 |
0.935 |
pander::pander(c(EFA_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
breaks = breaks,
main = "Correlation after EFA",
cexRow = 0.25,
cexCol = 0.25,
srtCol=35,
srtRow=75,
key.title=NA,
key.xlab="Spearman Correlation",
xlab="Feature", ylab="Feature")

#hist(cormat,freq=FALSE,
# density=NULL,
# xlim=c(-1,1),
# ylim=c(0,4.0),
# main="Correlation after EFA",xlab="Spearman Correlation")
EFADen <- density(cormat,from=-1,to=1)
par(op)
PCA Whitening
## Train Correlation
cormat <- cor(PCAWhite_Train,method="pearson")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
pander::pander(c(Train=max(abs(cormat))))
pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))
| 0.0217 |
0.0412 |
0.066 |
0.115 |
0.558 |
pander::pander(c(PCAWhite_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))
## Test Correlation
cormat <- cor(PCAWhitePredicted,method="spearman")
cormat[is.na(cormat)] <- 0
diag(cormat) <- 0;
pander::pander(c(Test=max(abs(cormat))))
pander::pander(quantile(abs(cormat),probs=c(0.9,0.95,0.975,0.99,0.999)))
| 0.0805 |
0.102 |
0.132 |
0.195 |
0.502 |
pander::pander(c(PCAWhite_fraction=sum(abs(cormat)>rhoThreshold)/ncol(cormat)/ncol(cormat)))
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
breaks = breaks,
main = "Correlation after PCAWhite",
cexRow = 0.25,
cexCol = 0.25,
srtCol=35,
srtRow=75,
key.title=NA,
key.xlab="Spearman Correlation",
xlab="Feature", ylab="Feature")

#hist(cormat,freq=FALSE,
# density=NULL,
# xlim=c(-1,1),
# ylim=c(0,4.0),
# main="Correlation after PCAWhite",xlab="Spearman Correlation")
WhiteDen <- density(cormat,from=-1,to=1)
par(op)
The Density
Plot
par(cex=0.7)
colors=c("red","blue","green","darkblue","darkgreen","purple","orange","darkred");
plot(rawDen,
xlim=c(-1,1),
ylim=c(0.001,7.0),
col=colors[1],
lty=1,
lwd=4,
log="y",
main="Test: Correlation Distribution",xlab="Spearman Correlation")
lines(DeDen,col=colors[2],lty=2,lwd=4)
lines(DeSpearDen,col=colors[3],lty=3,lwd=4)
lines(DeDrivDen,col=colors[4],lty=4,lwd=2)
lines(DeDrivSpearDen,col=colors[5],lty=5,lwd=2)
lines(PCADen,col=colors[6],lty=6,lwd=1)
lines(EFADen,col=colors[7],lty=7,lwd=1)
lines(WhiteDen,col=colors[8],lty=8,lwd=1)
names=c("Raw","IDeA:P","IDeA:S","DIDeA:P","DIDeA:S","PCA","EFA","White:PCA")
#colors=c("red","blue","green","blue","green","purple","purple","gray");
lines=c(1,2,3,4,5,6,7,8)
lwds=c(4,4,4,2,2,1,1,1)
legend("topleft",names,col=colors,lty=lines,lwd=lwds,cex=0.50)

par(op)
Differences
between train and test ROC AUC
par(op)
par(mfrow=c(1,1),cex=0.7)
AUCResults <- list();
diffAUC <- list();
thenames <- rownames(univar$orderframe)[(rownames(univar$orderframe) %in% colnames(testDataFrame))]
rawAUC <- univar$orderframe[thenames,"ROCAUC"]
thenames <- thenames[rawAUC >= aucTHR]
rawAUC <- univar$orderframe[thenames,"ROCAUC"]
rawAUCTest <- univarTest$orderframe[thenames,"ROCAUC"]
AUCResults$RAW <- rawAUCTest
diffAUC$RAW <- rawAUCTest-rawAUC
plot(rawAUC,rawAUCTest-rawAUC,
xlab="TRAIN ROC AUC",
ylab="Test:AUC-Train:AUC",
xlim=c(0.5,1.0),
ylim=c(-0.25,0.25),
pch=1,
col=colors[1],
main="ROC AUC Difference Between Test and Train")
thenames <- rownames(univarDe$orderframe)[!(rownames(univarDe$orderframe) %in% colnames(testDataFrame))]
IDeAP <- univarDe$orderframe[thenames,"ROCAUC"]
thenames <- thenames[IDeAP >= aucTHR]
IDeAP <- univarDe$orderframe[thenames,"ROCAUC"]
IDeAPTest <- univarDeTest$orderframe[thenames,"ROCAUC"]
AUCResults$IDeAP <- IDeAP
AUCResults$IDeAP_T <- IDeAPTest
diffAUC$IDeAP <- IDeAPTest-IDeAP
points(IDeAP,IDeAPTest-IDeAP,pch=2,col=colors[2])
thenames <- rownames(univarDeSpear$orderframe)[!(rownames(univarDeSpear$orderframe) %in% colnames(testDataFrame))]
IDeAS <- univarDeSpear$orderframe[thenames,"ROCAUC"]
thenames <- thenames[IDeAS >= aucTHR]
IDeAS <- univarDeSpear$orderframe[thenames,"ROCAUC"]
IDeASTest <- univarDeSpearTest$orderframe[thenames,"ROCAUC"]
AUCResults$IDeAS <- IDeAS
AUCResults$IDeAS_T <- IDeASTest
diffAUC$IDeAS <- IDeASTest-IDeAS
points(IDeAS,IDeASTest-IDeAS,pch=3,col=colors[3])
thenames <- rownames(univarDeDri$orderframe)[!(rownames(univarDeDri$orderframe) %in% colnames(testDataFrame))]
DIDeAP <- univarDeDri$orderframe[thenames,"ROCAUC"]
thenames <- thenames[DIDeAP >= aucTHR]
DIDeAP <- univarDeDri$orderframe[thenames,"ROCAUC"]
DIDeAPTest <- univarDeDriTest$orderframe[thenames,"ROCAUC"]
AUCResults$DIDeAP <- DIDeAP
AUCResults$DIDeAP_T <- DIDeAPTest
diffAUC$DIDeAP <- DIDeAPTest-DIDeAP
points(DIDeAP,DIDeAPTest-DIDeAP,pch=4,col=colors[4])
thenames <- rownames(univarDeDriSpear$orderframe)[!(rownames(univarDeDriSpear$orderframe) %in% colnames(testDataFrame))]
DIDeAS <- univarDeDriSpear$orderframe[thenames,"ROCAUC"]
thenames <- thenames[DIDeAS >= aucTHR]
DIDeAS <- univarDeDriSpear$orderframe[thenames,"ROCAUC"]
DIDeASTest <- univarDeDriSpearTest$orderframe[thenames,"ROCAUC"]
AUCResults$DIDeAS <- DIDeAS
AUCResults$DIDeAS_T <- DIDeASTest
diffAUC$DIDeAS <- DIDeASTest-DIDeAS
points(DIDeAS,DIDeASTest-DIDeAS,pch=5,col=colors[5])
thenames <- rownames(univarPCA$orderframe)[!(rownames(univarPCA$orderframe) %in% colnames(testDataFrame))]
PCA <- univarPCA$orderframe[thenames,"ROCAUC"]
thenames <- thenames[PCA >= aucTHR]
PCA <- univarPCA$orderframe[thenames,"ROCAUC"]
PCATest <- univarPCATest$orderframe[thenames,"ROCAUC"]
AUCResults$PCA <- PCA
AUCResults$PCA_T <- PCATest
diffAUC$PCA <- PCATest-PCA
points(PCA,PCATest-PCA,pch=6,col=colors[6])
thenames <- rownames(univarEFA$orderframe)[!(rownames(univarEFA$orderframe) %in% colnames(testDataFrame))]
EFA <- univarEFA$orderframe[thenames,"ROCAUC"]
thenames <- thenames[EFA >= aucTHR]
EFA <- univarEFA$orderframe[thenames,"ROCAUC"]
EFATest <- univarEFATest$orderframe[thenames,"ROCAUC"]
AUCResults$EFA <- EFA
AUCResults$EFA_T <- EFATest
diffAUC$EFA <- EFATest-EFA
points(EFA,EFATest-EFA,pch=7,col=colors[7])
thenames <- rownames(univarWhite$orderframe)[!(rownames(univarWhite$orderframe) %in% colnames(testDataFrame))]
WPCA <- univarWhite$orderframe[thenames,"ROCAUC"]
thenames <- thenames[WPCA >= aucTHR]
WPCA <- univarWhite$orderframe[thenames,"ROCAUC"]
WPCATest <- univarWhiteTest$orderframe[thenames,"ROCAUC"]
AUCResults$WPCA <- WPCA
AUCResults$WPCA_T <- WPCATest
diffAUC$WPCA <- WPCATest-WPCA
points(WPCA,WPCATest-WPCA,pch=8,col=colors[8])
names=c("Raw","IDeA:P","IDeA:S","DIDeA:P","DIDeA:S","PCA","EFA","White:PCA")
pchs=c(1,2,3,4,5,6,7,8)
legend("bottomright",names,col=colors,pch=pchs,cex=0.50)

par(op)
Violin of
differences
par(op)
par(mfrow=c(1,1),cex=0.7)
vioplot(diffAUC,
ylim=c(-0.25,0.25),
ylab="Test-Train",
main="Test-Train Paired ROC AUC Difference",
col=colors,
cex.axis=0.6,
las=2
)
stripchart(diffAUC, method = "jitter", col = "gray",
vertical = TRUE, pch = 1, add = TRUE,cex=0.35)
points(1:length(diffAUC),lapply(diffAUC,mean), pch = 18, col = "yellow", cex = 2.25)

par(op)
Distribution of
ROC AUC in latent Variables
par(op)
par(mfrow=c(1,1),cex=0.7)
colors2 <- length(AUCResults)
colors2[1] <- colors[1];
colors2[2*(1:(length(colors)-1))] <- colors[2:length(colors)]
colors2[1+2*(1:(length(colors)-1))] <- colors[2:length(colors)]
vioplot(AUCResults,
ylim=c(0.3,1.0),
ylab="ROC AUC",
main="ROC AUC of Latent Variables",
col=colors2,
cex.axis=0.6,
las=2
)
abline(h=0.5,col="black")
stripchart(AUCResults, method = "jitter", col = "gray",
vertical = TRUE, pch = 1, add = TRUE,cex=0.35)
points(1:length(AUCResults),lapply(AUCResults,mean), pch = 18, col = "yellow", cex = 2.25)

par(op)
Differences
between train and test Balanced Accuracy
par(op)
par(mfrow=c(1,1),cex=0.7)
BACCTHR <- aucTHR
BACCResults <- list();
diffBACC <- list();
thenames <- rownames(univar$orderframe)[(rownames(univar$orderframe) %in% colnames(testDataFrame))]
rawBACC <- univar$orderframe[thenames,"BACC"]
thenames <- thenames[rawBACC >= BACCTHR]
rawBACC <- univar$orderframe[thenames,"BACC"]
rawBACCTest <- univarTest$orderframe[thenames,"BACC"]
BACCResults$RAW <- rawBACCTest
diffBACC$RAW <- rawBACCTest-rawBACC
plot(rawBACC,rawBACCTest-rawBACC,
xlab="TRAIN Balanced Acc",
ylab="Test:BACC-Train:BACC",
xlim=c(0.5,1.0),
ylim=c(-0.25,0.25),
pch=1,
col=colors[1],
main="Balanced Acc Difference Between Test and Train")
thenames <- rownames(univarDe$orderframe)[!(rownames(univarDe$orderframe) %in% colnames(testDataFrame))]
IDeAP <- univarDe$orderframe[thenames,"BACC"]
thenames <- thenames[IDeAP >= BACCTHR]
IDeAP <- univarDe$orderframe[thenames,"BACC"]
IDeAPTest <- univarDeTest$orderframe[thenames,"BACC"]
BACCResults$IDeAP <- IDeAP
BACCResults$IDeAP_T <- IDeAPTest
diffBACC$IDeAP <- IDeAPTest-IDeAP
points(IDeAP,IDeAPTest-IDeAP,pch=2,col=colors[2])
thenames <- rownames(univarDeSpear$orderframe)[!(rownames(univarDeSpear$orderframe) %in% colnames(testDataFrame))]
IDeAS <- univarDeSpear$orderframe[thenames,"BACC"]
thenames <- thenames[IDeAS >= BACCTHR]
IDeAS <- univarDeSpear$orderframe[thenames,"BACC"]
IDeASTest <- univarDeSpearTest$orderframe[thenames,"BACC"]
BACCResults$IDeAS <- IDeAS
BACCResults$IDeAS_T <- IDeASTest
diffBACC$IDeAS <- IDeASTest-IDeAS
points(IDeAS,IDeASTest-IDeAS,pch=3,col=colors[3])
thenames <- rownames(univarDeDri$orderframe)[!(rownames(univarDeDri$orderframe) %in% colnames(testDataFrame))]
DIDeAP <- univarDeDri$orderframe[thenames,"BACC"]
thenames <- thenames[DIDeAP >= BACCTHR]
DIDeAP <- univarDeDri$orderframe[thenames,"BACC"]
DIDeAPTest <- univarDeDriTest$orderframe[thenames,"BACC"]
BACCResults$DIDeAP <- DIDeAP
BACCResults$DIDeAP_T <- DIDeAPTest
diffBACC$DIDeAP <- DIDeAPTest-DIDeAP
points(DIDeAP,DIDeAPTest-DIDeAP,pch=4,col=colors[4])
thenames <- rownames(univarDeDriSpear$orderframe)[!(rownames(univarDeDriSpear$orderframe) %in% colnames(testDataFrame))]
DIDeAS <- univarDeDriSpear$orderframe[thenames,"BACC"]
thenames <- thenames[DIDeAS >= BACCTHR]
DIDeAS <- univarDeDriSpear$orderframe[thenames,"BACC"]
DIDeASTest <- univarDeDriSpearTest$orderframe[thenames,"BACC"]
BACCResults$DIDeAS <- DIDeAS
BACCResults$DIDeAS_T <- DIDeASTest
diffBACC$DIDeAS <- DIDeASTest-DIDeAS
points(DIDeAS,DIDeASTest-DIDeAS,pch=5,col=colors[5])
thenames <- rownames(univarPCA$orderframe)[!(rownames(univarPCA$orderframe) %in% colnames(testDataFrame))]
PCA <- univarPCA$orderframe[thenames,"BACC"]
thenames <- thenames[PCA >= BACCTHR]
PCA <- univarPCA$orderframe[thenames,"BACC"]
PCATest <- univarPCATest$orderframe[thenames,"BACC"]
BACCResults$PCA <- PCA
BACCResults$PCA_T <- PCATest
diffBACC$PCA <- PCATest-PCA
points(PCA,PCATest-PCA,pch=6,col=colors[6])
thenames <- rownames(univarEFA$orderframe)[!(rownames(univarEFA$orderframe) %in% colnames(testDataFrame))]
EFA <- univarEFA$orderframe[thenames,"BACC"]
thenames <- thenames[EFA >= BACCTHR]
EFA <- univarEFA$orderframe[thenames,"BACC"]
EFATest <- univarEFATest$orderframe[thenames,"BACC"]
BACCResults$EFA <- EFA
BACCResults$EFA_T <- EFATest
diffBACC$EFA <- EFATest-EFA
points(EFA,EFATest-EFA,pch=7,col=colors[7])
thenames <- rownames(univarWhite$orderframe)[!(rownames(univarWhite$orderframe) %in% colnames(testDataFrame))]
WPCA <- univarWhite$orderframe[thenames,"BACC"]
thenames <- thenames[WPCA >= BACCTHR]
WPCA <- univarWhite$orderframe[thenames,"BACC"]
WPCATest <- univarWhiteTest$orderframe[thenames,"BACC"]
BACCResults$WPCA <- WPCA
BACCResults$WPCA_T <- WPCATest
diffBACC$WPCA <- WPCATest-WPCA
points(WPCA,WPCATest-WPCA,pch=8,col=colors[8])
names=c("Raw","IDeA:P","IDeA:S","DIDeA:P","DIDeA:S","PCA","EFA","White:PCA")
pchs=c(1,2,3,4,5,6,7,8)
legend("bottomright",names,col=colors,pch=pchs,cex=0.50)

par(op)
Violin of
differences
par(op)
par(mfrow=c(1,1),cex=0.7)
vioplot(diffBACC,
ylim=c(-0.25,0.25),
ylab="Test-Train",
main="Test-Train Paired Balanced Acc Difference",
col=colors,
cex.axis=0.6,
las=2
)
stripchart(diffBACC, method = "jitter", col = "gray",
vertical = TRUE, pch = 1, add = TRUE,cex=0.35)
points(1:length(diffBACC),lapply(diffBACC,mean), pch = 18, col = "yellow", cex = 2.25)

par(op)
Distribution of
Balanced Acc in latent Variables
par(op)
par(mfrow=c(1,1),cex=0.7)
colors2 <- length(BACCResults)
colors2[1] <- colors[1];
colors2[2*(1:(length(colors)-1))] <- colors[2:length(colors)]
colors2[1+2*(1:(length(colors)-1))] <- colors[2:length(colors)]
vioplot(BACCResults,
ylim=c(0.3,1.0),
ylab="Balanced Acc",
main="Balanced Acc of Latent Variables",
col=colors2,
cex.axis=0.6,
las=2
)
abline(h=0.5,col="black")
stripchart(BACCResults, method = "jitter", col = "gray",
vertical = TRUE, pch = 1, add = TRUE,cex=0.35)
points(1:length(BACCResults),lapply(BACCResults,mean), pch = 18, col = "yellow", cex = 2.25)

par(op)
Differences
between train and test IDI
par(op)
par(mfrow=c(1,1),cex=0.7)
testIDI <- list();
diffIDI <- list();
rawIDI <- univar$orderframe$IDI
rawIDITest <- univarTest$orderframe$IDI
testIDI$RAW <- rawIDITest
diffIDI$RAW <- rawIDITest-rawIDI
plot(rawIDI,rawIDITest-rawIDI,
xlab="TRAIN Test IDI",
ylab="Test:IDI-Train:IDI",
xlim=c(0,0.5),
ylim=c(-0.2,0.2),
pch=1,
col=colors[1],
main="Predict IDI Difference Between Test and Train")
IDeAP <- univarDe$orderframe$IDI[!(rownames(univarDe$orderframe) %in% colnames(testDataFrame))]
IDeAPTest <-univarDeTest$orderframe$IDI[!(rownames(univarDe$orderframe) %in% colnames(testDataFrame))]
testIDI$IDeAP <- IDeAP
testIDI$IDeAP_T <- IDeAPTest
diffIDI$IDeAP <- IDeAPTest-IDeAP
points(IDeAP,IDeAPTest-IDeAP,pch=2,col=colors[2])
IDeAS <- univarDeSpear$orderframe$IDI[!(rownames(univarDeSpearTest$orderframe) %in% colnames(testDataFrame))]
IDeASTest <- univarDeSpearTest$orderframe$IDI[!(rownames(univarDeSpearTest$orderframe) %in% colnames(testDataFrame))]
testIDI$IDeAS <- IDeAS
testIDI$IDeAS_T <- IDeASTest
diffIDI$IDeAS <- IDeASTest-IDeAS
points(IDeAS,IDeASTest-IDeAS,pch=3,col=colors[3])
DIDeAP <- univarDeDri$orderframe$IDI[!(rownames(univarDeDriTest$orderframe) %in% colnames(testDataFrame))]
DIDeAPTest <- univarDeDriTest$orderframe$IDI[!(rownames(univarDeDriTest$orderframe) %in% colnames(testDataFrame))]
testIDI$DIDeAP <- DIDeAP
testIDI$DIDeAP_T <- DIDeAPTest
diffIDI$DIDeAP <- DIDeAPTest-DIDeAP
points(DIDeAP,DIDeAPTest-DIDeAP,pch=4,col=colors[4])
DIDeAS <- univarDeDriSpear$orderframe$IDI[!(rownames(univarDeDriSpearTest$orderframe) %in% colnames(testDataFrame))]
DIDeASTest <- univarDeDriSpearTest$orderframe$IDI[!(rownames(univarDeDriSpearTest$orderframe) %in% colnames(testDataFrame))]
testIDI$DIDeAS <- DIDeAS
testIDI$DIDeAS_T <- DIDeASTest
diffIDI$DIDeAS <- DIDeASTest-DIDeAS
points(DIDeAS,DIDeASTest-DIDeAS,pch=5,col=colors[5])
PCA <- univarPCA$orderframe$IDI[!(rownames(univarPCA$orderframe) %in% colnames(testDataFrame))]
PCATest <- univarPCATest$orderframe$IDI[!(rownames(univarPCA$orderframe) %in% colnames(testDataFrame))]
testIDI$PCA <- PCA
testIDI$PCA_T <- PCATest
diffIDI$PCA <- PCATest-PCA
points(PCA,PCATest-PCA,pch=6,col=colors[6])
EFA <- univarEFA$orderframe$IDI[!(rownames(univarEFA$orderframe) %in% colnames(testDataFrame))]
EFATest <- univarEFATest$orderframe$IDI[!(rownames(univarEFA$orderframe) %in% colnames(testDataFrame))]
testIDI$EFA <- EFA
testIDI$EFA_T <- EFATest
diffIDI$EFA <- EFATest-EFA
points(EFA,EFATest-EFA,pch=7,col=colors[7])
WPCA <- univarWhite$orderframe$IDI[!(rownames(univarWhite$orderframe) %in% colnames(testDataFrame))]
WPCATest <- univarWhiteTest$orderframe$IDI[!(rownames(univarWhite$orderframe) %in% colnames(testDataFrame))]
testIDI$WPCA <- WPCA
testIDI$WPCA_T <- WPCATest
diffIDI$WPCA <- WPCATest-WPCA
points(WPCA,WPCATest-WPCA,pch=8,col=colors[8])
names=c("Raw","IDeA:P","IDeA:S","DIDeA:P","DIDeA:S","PCA","EFA","White:PCA")
pchs=c(1,2,3,4,5,6,7,8)
legend("bottomright",names,col=colors,pch=pchs,cex=0.50)

par(op)
Violin of
differences
par(op)
par(mfrow=c(1,1),cex=0.7)
vioplot(diffIDI,
ylim=c(-0.2,0.2),
ylab="Test-Train",
main="Test-Train Paired Predict IDI Difference",
col=colors,
cex.axis=0.6,
las=2
)
stripchart(diffIDI, method = "jitter", col = "gray",
vertical = TRUE, pch = 1, add = TRUE,cex=0.35)
points(1:length(diffIDI),lapply(diffIDI,mean), pch = 18, col = "yellow", cex = 2.25)

par(op)
Distribution of
Predict IDI in latent Variables
par(op)
par(mfrow=c(1,1),cex=0.7)
colors2 <- length(testIDI)
colors2[1] <- colors[1];
colors2[2*(1:(length(colors)-1))] <- colors[2:length(colors)]
colors2[1+2*(1:(length(colors)-1))] <- colors[2:length(colors)]
vioplot(testIDI,
ylim=c(0.0,0.5),
ylab="Predict IDI",
main="Predict IDI of Latent Variables",
col=colors2,
cex.axis=0.6,
las=2
)
stripchart(testIDI, method = "jitter", col = "gray",
vertical = TRUE, pch = 1, add = TRUE,cex=0.35)
points(1:length(testIDI),lapply(testIDI,mean), pch = 18, col = "yellow", cex = 2.25)

par(op)
The tables
pander::pander(univarTest$orderframe[1:TopVariables,univariate_columns])
| IT_CCV_HF |
2.70e-03 |
5.51e-02 |
5.11e-01 |
7.00e-01 |
0.00e+00 |
0.482 |
0.873 |
0.865 |
| IT_CCV_LF |
1.01e-02 |
2.04e-01 |
1.66e+00 |
1.85e+00 |
1.11e-16 |
0.497 |
0.873 |
0.865 |
| IT_HF_prctile25 |
8.77e-03 |
1.97e-01 |
4.69e+00 |
5.58e+01 |
0.00e+00 |
0.414 |
0.833 |
0.864 |
| IT_PSD_min |
9.19e-08 |
2.10e-06 |
2.39e-05 |
4.55e-05 |
0.00e+00 |
0.413 |
0.833 |
0.864 |
| IT_HF_median |
1.74e-02 |
3.90e-01 |
9.48e+00 |
1.14e+02 |
0.00e+00 |
0.407 |
0.833 |
0.864 |
pander::pander(univarDeTest$orderframe[1:TopVariables,univariate_columns])
| La_EDA_processed_baseline |
2.89e-02 |
4.14e-02 |
-1.11e-02 |
5.41e-02 |
0 |
0.232 |
0.861 |
0.864 |
| La_IT_VLF_trimmean25 |
-2.80e-17 |
6.33e-16 |
-4.97e-15 |
1.51e-14 |
0 |
0.402 |
0.833 |
0.864 |
| La_IT_LF_mean_D |
5.26e-05 |
1.19e-03 |
9.32e-03 |
2.80e-02 |
0 |
0.402 |
0.833 |
0.864 |
| IT_LF_max_D |
5.94e-01 |
1.34e+01 |
1.05e+02 |
3.17e+02 |
0 |
0.402 |
0.833 |
0.864 |
| La_IT_LF_median_D |
-1.06e-16 |
2.41e-15 |
-1.57e-14 |
4.36e-14 |
0 |
0.390 |
0.833 |
0.859 |
pander::pander(univarDeSpearTest$orderframe[1:TopVariables,univariate_columns])
| La_IT_BRV_max |
1.24e-02 |
6.11e-01 |
4.73e+00 |
8.95e+00 |
0 |
0.506 |
0.912 |
0.901 |
| IT_VLF_mean |
7.19e-01 |
1.64e+01 |
1.22e+02 |
3.41e+02 |
0 |
0.399 |
0.833 |
0.864 |
| La_IT_BRV_mad |
8.95e-03 |
1.66e-01 |
1.68e+00 |
2.10e+00 |
0 |
0.467 |
0.866 |
0.862 |
| La_IT_LF_prctile25_D |
-1.07e-16 |
2.41e-15 |
-2.22e-14 |
8.13e-14 |
0 |
0.408 |
0.833 |
0.861 |
| La_IT_CCV_LF |
6.64e-03 |
1.27e-01 |
1.08e+00 |
1.32e+00 |
0 |
0.461 |
0.866 |
0.861 |
pander::pander(univarDeDriTest$orderframe[1:TopVariables,univariate_columns])
| IT_CCV_HF |
2.70e-03 |
5.51e-02 |
5.11e-01 |
7.00e-01 |
0 |
0.482 |
0.873 |
0.865 |
| La_EDA_processed_baseline |
2.89e-02 |
4.14e-02 |
-1.11e-02 |
5.41e-02 |
0 |
0.232 |
0.861 |
0.864 |
| La_IT_VLF_trimmean25 |
-2.80e-17 |
6.33e-16 |
-4.97e-15 |
1.51e-14 |
0 |
0.402 |
0.833 |
0.864 |
| La_IT_LF_mean_D |
5.26e-05 |
1.19e-03 |
9.32e-03 |
2.80e-02 |
0 |
0.402 |
0.833 |
0.864 |
| La_IT_LF_median_D |
-1.06e-16 |
2.41e-15 |
-1.57e-14 |
4.36e-14 |
0 |
0.390 |
0.833 |
0.859 |
pander::pander(univarDeDriSpearTest$orderframe[1:TopVariables,univariate_columns])
| La_IT_LF_median_D |
-4.32e-32 |
8.80e-31 |
-8.17e-30 |
1.12e-29 |
0 |
0.482 |
0.873 |
0.865 |
| La_IT_LF_prctile25_D |
-2.36e-23 |
4.80e-22 |
-4.46e-21 |
6.10e-21 |
0 |
0.482 |
0.873 |
0.865 |
| IT_CCV_HF |
2.70e-03 |
5.51e-02 |
5.11e-01 |
7.00e-01 |
0 |
0.482 |
0.873 |
0.865 |
| La_IT_MF_min |
-3.69e-01 |
7.26e+00 |
-6.22e+01 |
1.63e+02 |
0 |
0.386 |
0.866 |
0.863 |
| La_IT_HF_max |
-3.72e-01 |
7.37e+00 |
-6.16e+01 |
1.60e+02 |
0 |
0.386 |
0.868 |
0.863 |
pander::pander(univarPCATest$orderframe[1:TopVariables,univariate_columns])
| RC15 |
-3.64 |
2.196 |
10.95 |
20.68 |
0.00e+00 |
0.604 |
0.945 |
0.956 |
| RC8 |
7.23 |
5.907 |
-21.74 |
21.41 |
2.88e-09 |
0.621 |
0.935 |
0.942 |
| RC2 |
-10.47 |
5.317 |
31.48 |
115.68 |
0.00e+00 |
0.536 |
0.900 |
0.920 |
| IT_BRV_kurtosis |
1.02 |
0.478 |
5.18 |
27.79 |
0.00e+00 |
0.453 |
0.833 |
0.834 |
| RC7 |
1.79 |
22.207 |
-5.39 |
2.96 |
0.00e+00 |
0.180 |
0.819 |
0.816 |
pander::pander(univarEFATest$orderframe[1:TopVariables,univariate_columns])
| MR15 |
-3.62 |
2.182 |
10.88 |
20.62 |
0.00e+00 |
0.603 |
0.945 |
0.956 |
| MR8 |
7.23 |
5.927 |
-21.75 |
21.44 |
3.06e-09 |
0.621 |
0.935 |
0.942 |
| MR2 |
-10.46 |
5.315 |
31.47 |
115.67 |
0.00e+00 |
0.536 |
0.900 |
0.920 |
| IT_BRV_kurtosis |
1.02 |
0.478 |
5.18 |
27.79 |
0.00e+00 |
0.453 |
0.833 |
0.834 |
| MR7 |
1.79 |
22.166 |
-5.37 |
2.95 |
0.00e+00 |
0.178 |
0.818 |
0.815 |
pander::pander(univarWhiteTest$orderframe[1:TopVariables,univariate_columns])
| IT_BRV_kurtosis |
1.022 |
0.47781 |
5.183 |
27.7859 |
0.00000 |
0.453 |
0.833 |
0.834 |
| IT_MF_kurtosis |
1.703 |
0.00280 |
1.672 |
0.0727 |
0.00000 |
0.328 |
0.772 |
0.796 |
| L56 |
-0.440 |
0.79454 |
0.544 |
1.1913 |
0.00113 |
0.231 |
0.769 |
0.768 |
| IT_p_Total_kurtosis |
1.669 |
0.00865 |
1.755 |
0.2017 |
0.00000 |
0.246 |
0.736 |
0.759 |
| EDA_processed_baseline_D |
0.306 |
0.92509 |
-0.664 |
1.6655 |
0.00000 |
0.146 |
0.740 |
0.749 |
topUni <- univar$orderframe$Name[1:TopVariables]
topDe <- univarDe$orderframe$Name[1:TopVariables]
topDeSpear <- univarDeSpear$orderframe$Name[1:TopVariables]
topDeDri <- univarDeDri$orderframe$Name[1:TopVariables]
topDeDriSpear <- univarDeDriSpear$orderframe$Name[1:TopVariables]
topPCA <- univarPCA$orderframe$Name[1:TopVariables]
topEFA <- univarEFA$orderframe$Name[1:TopVariables]
topPCAWhite <- univarWhite$orderframe$Name[1:TopVariables]
Model of top
variables
par(mfrow=c(1,2),cex=0.6)
lmRAW <- glm(paste(outcome,"~."),
trainDataFrame[,c(outcome,topUni)],
family="binomial")
prRaw <- predictionStats_binary(cbind(testDataFrame[,outcome],predict(lmRAW,testDataFrame)),"Top Raw",cex=0.75)

lmDe <- glm(paste(outcome,"~."),
DEdataframe[,c(outcome,topDe)],
family="binomial")
prDe <- predictionStats_binary(cbind(predTestDe[,outcome],predict(lmDe,predTestDe)),"Top IDeA:P",cex=0.75)

lmDeSpear <- glm(paste(outcome,"~."),
DEdataframeSpear[,c(outcome,topDeSpear)],
family="binomial")
prSpear <- predictionStats_binary(cbind(predTestDeSpear[,outcome],predict(lmDeSpear,predTestDeSpear)),"Top IDeA:S",cex=0.75)

lmDri <- glm(paste(outcome,"~."),
DriDEdataframe[,c(outcome,topDeDri)],
family="binomial")
prDri <- predictionStats_binary(cbind(predTestDe[,outcome],predict(lmDri,predTestDri)),"Top DIDeA:P",cex=0.75)

lmDriSpear <- glm(paste(outcome,"~."),
DriDEdataframeSpear[,c(outcome,topDeDriSpear)],
family="binomial")
prDriSpear <- predictionStats_binary(cbind(predTestDriSpear[,outcome],predict(lmDriSpear,predTestDriSpear)),"Top DIDeA:S",cex=0.7)

lmPCA <- glm(paste(outcome,"~."),
PCA_Train[,c(outcome,topPCA)],
family="binomial")
prPCA <- predictionStats_binary(cbind(PCA_Predicted[,outcome],predict(lmPCA,PCA_Predicted)),"Top PCA",cex=0.75)

lmEFA <- glm(paste(outcome,"~."),
EFA_Train[,c(outcome,topEFA)],
family="binomial")
prEFA <- predictionStats_binary(cbind(EFA_Predicted[,outcome],predict(lmEFA,EFA_Predicted)),"Top EFA",cex=0.75)

lmPCAW <- glm(paste(outcome,"~."),
PCAWhite_Train[,c(outcome,topPCAWhite)],
family="binomial")
prWPCA <- predictionStats_binary(cbind(PCAWhitePredicted[,outcome],predict(lmPCAW,PCAWhitePredicted)),"Top White:PCA",cex=0.75)

par(op)
The Performance
Tables and Plots
par(cex=0.6)
aucs <- prRaw$aucs
aucs <- rbind(aucs,prDe$aucs)
aucs <- rbind(aucs,prSpear$aucs)
aucs <- rbind(aucs,prDri$aucs)
aucs <- rbind(aucs,prDriSpear$aucs)
aucs <- rbind(aucs,prPCA$aucs)
aucs <- rbind(aucs,prEFA$aucs)
aucs <- rbind(aucs,prWPCA$aucs)
rownames(aucs) <- c("RAW",
"IDeA:P",
"IDeA:S",
"DIDeA:P",
"DIDeA:S",
"PCA",
"EFA",
"WPCA"
)
pander::pander(aucs)
| RAW |
0.861 |
0.839 |
0.883 |
| IDeA:P |
0.938 |
0.923 |
0.952 |
| IDeA:S |
0.908 |
0.890 |
0.926 |
| DIDeA:P |
0.945 |
0.932 |
0.959 |
| DIDeA:S |
0.870 |
0.849 |
0.891 |
| PCA |
0.942 |
0.930 |
0.953 |
| EFA |
0.942 |
0.930 |
0.953 |
| WPCA |
0.899 |
0.880 |
0.917 |
bpAUC <- barPlotCiError(as.matrix(aucs),
metricname = "ROC AUC",
thesets = "Test AUC",
themethod = rownames(aucs),
main = "ROC AUC",
offsets = c(0.5,1),
scoreDirection = ">",
ho=0.5,
args.legend = list(bg = "white",x="bottomleft",inset=c(0.0,0),cex=0.5),
col = terrain.colors(nrow(aucs))
)

berror <- prRaw$berror
berror <- rbind(berror,prDe$berror)
berror <- rbind(berror,prSpear$berror)
berror <- rbind(berror,prDri$berror)
berror <- rbind(berror,prDriSpear$berror)
berror <- rbind(berror,prPCA$berror)
berror <- rbind(berror,prEFA$berror)
berror <- rbind(berror,prWPCA$berror)
rownames(berror) <- rownames(aucs)
pander::pander(berror)
| RAW |
0.210 |
0.193 |
0.227 |
| IDeA:P |
0.222 |
0.198 |
0.241 |
| IDeA:S |
0.199 |
0.179 |
0.218 |
| DIDeA:P |
0.188 |
0.168 |
0.208 |
| DIDeA:S |
0.215 |
0.195 |
0.235 |
| PCA |
0.165 |
0.144 |
0.184 |
| EFA |
0.164 |
0.145 |
0.188 |
| WPCA |
0.191 |
0.169 |
0.208 |
bpBER <- barPlotCiError(as.matrix(berror),
metricname = "Balanced Error Rate",
thesets = "Test BER",
themethod = rownames(aucs),
main = "Balanced Error Rate",
offsets = c(0.5,1),
scoreDirection = "<",
ho=0.5,
args.legend = list(bg = "white",x="topleft",inset=c(0.0,0),cex=0.5),
col = terrain.colors(nrow(aucs))
)

par(op)